Comparison of Trajectory Estimation Methods Based on LIDAR and Monocular Camera in a Simulated Environment

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

  • Amanda Dias Evangelista
  • Vinícius Barbosa Schettino
  • Murillo Ferreira dos Santos
  • Paolo Mercorelli

Odometry estimation is a fundamental problem for two-dimensional locomotion in mobile robotics. When common motion sensors, such as wheel encoders, are absent or unreliable, other sensors can be employed for the same task. LIDARs are a common choice due to their precision but have relatively high cost. The monocular camera provides a cost-effective alternative but has limitations, such as reliance on lighting conditions and the absence of direct depth information. Both sensors face specific challenges when employed in indoor environments with SLAM algorithms for odometry estimation. This article proposes a comparative analysis between the Hector SLAM algorithm, based on LIDAR, and the ORB-SLAM3 algorithm, based on a monocular camera, to assess the accuracy of estimated trajectories. The sensors were mounted on a wheeled robot in a simulated environment and the simulator provided the ground truth trajectories. As expected, it was observed that the LIDAR-based algorithm performed better than the camera-based one, but the latter is an acceptable replacement for odometry estimation when the trajectories are simple and executed at lower speeds.

Original languageEnglish
Title of host publicationProceedings of the 2024 25th International Carpathian Control Conference, ICCC 2024
EditorsAndrzej Kot
Number of pages6
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date22.05.2024
ISBN (print)9798350350692
ISBN (electronic)979-8-3503-5070-8, 979-8-3503-5069-2
DOIs
Publication statusPublished - 22.05.2024
Event25th International Carpathian Control Conference - ICCC 2024 - Hotel Krynica****, Krynica Zdroj, Poland
Duration: 22.05.202424.05.2024
Conference number: 25
https://iccc.agh.edu.pl/#top

Bibliographical note

Publisher Copyright:
©2024 IEEE.

    Research areas

  • Mobile Robotics, Odometry, Visual SLAM
  • Engineering

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